This paper provides the technical details of developing models to enable automated stage-wise analyses to be implemented within the real-time completion (RTC) analytics system. The models—two of which use machine learning (ML), including the convolutional neural network (CNN) technique (LeCun et al. 1990) and the U-Net architecture (Ronneberger et al. 2015)—detect the hydraulic fracture stage start and end, identify the ball seat operation, and categorize periods of pump rate. These tasks are performed on the basis of the two reliably available measurements of slurry rate and wellhead pressure, which enable the models to run automatically in real time, and also lay the foundation for further hydraulic fracturing advanced analyses. The presented solution provides real-time automated interpretations of hydraulic fracture events, enabling auto-generation of key performance indicator (KPI) reports, dispelling the need for manual labeling, and eliminating human bias and errors. It replaces the manual tasks in the RTC workflow/data pipeline and paves the way for a fully automated RTC system.

本文提供了开发模型的技术细节，以使自动化的分阶段分析能够在实时完成（RTC）分析系统中实施。这些模型（其中两个使用机器学习（ML），包括卷积神经网络（CNN）技术（LeCun等人，1990）和U-Net体系结构（Ronneberger等人，2015）来检测水力压裂阶段的开始和结束。最后，确定球座的运行方式，并对泵速周期进行分类。这些任务是基于两个可靠的泥浆速度和井口压力测量结果执行的，这些测量值使模型能够实时自动运行，也为进一步的水力压裂高级分析奠定了基础。提出的解决方案可实时自动解释水力压裂事件，支持自动生成关键绩效指标（KPI）报告，消除了手动标记的需要，并消除了人为的偏见和错误。它取代了RTC工作流/数据管道中的手动任务，并为全自动RTC系统铺平了道路。